Li, J.; Pharr, G. M.; Kirchlechner, C.: Quantitative insights into the dislocation source behavior of twin boundaries suggest a new dislocation source mechanism. Journal of Materials Research 36 (10), pp. 2037 - 2046 (2021)
Luo, W.; Kirchlechner, C.; Li, J.; Dehm, G.; Stein, F.: Composition dependence of hardness and elastic modulus of the cubic and hexagonal NbCo2 Laves phase polytypes studied by nanoindentation. Journal of Materials Research 35 (2), pp. 185 - 195 (2020)
Qin, Y.; Li, J.; Herbig, M.: Microstructural origin of the outstanding durability of the high nitrogen bearing steel X30CrMoN15-1. Materials Characterization 159, 110049 (2020)
Li, J.; Dehm, G.; Kirchlechner, C.: Dislocation source activation by nanoindentation in single crystals and at grain boundaries. E-MRS Spring, Strasbourg, France (2018)
Li, J.; Dehm, G.; Kirchlechner, C.: Differences in dislocation source activation stress in the grain interior and at twin boundaries using nanoindentation. Nanobruecken 2018, Erlangen, Germany (2018)
Li, J.; Dehm, G.; Kirchlechner, C.: Grain Boundaries acting as dislocation sources. Gordon Research Seminar "Thin Film & Small Scale Mechanical Behavior", Lewiston, ME, USA (2018)
Li, J.: Probing dislocation nucleation in grains and at Ʃ3 twin boundaries of Cu alloys by nanoindentation. Dissertation, Ruhr-Universität Bochum (2020)
Scientists of the Max-Planck-Institut für Eisenforschung pioneer new machine learning model for corrosion-resistant alloy design. Their results are now published in the journal Science Advances
Data-rich experiments such as scanning transmission electron microscopy (STEM) provide large amounts of multi-dimensional raw data that encodes, via correlations or hierarchical patterns, much of the underlying materials physics. With modern instrumentation, data generation tends to be faster than human analysis, and the full information content is…